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Computer Vision Self-supervised Learning Methods on Time Series (2109.00783v4)
Published 2 Sep 2021 in cs.LG, cs.AI, and stat.ML
Abstract: Self-supervised learning (SSL) has had great success in both computer vision. Most of the current mainstream computer vision SSL frameworks are based on Siamese network architecture. These approaches often rely on cleverly crafted loss functions and training setups to avoid feature collapse. In this study, we evaluate if those computer-vision SSL frameworks are also effective on a different modality (\textit{i.e.,} time series). The effectiveness is experimented and evaluated on the UCR and UEA archives, and we show that the computer vision SSL frameworks can be effective even for time series. In addition, we propose a new method that improves on the recently proposed VICReg method. Our method improves on a \textit{covariance} term proposed in VICReg, and in addition we augment the head of the architecture by an iterative normalization layer that accelerates the convergence of the model.
- Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR (3) He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020) (4) Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A., Guo, Z.D., Azar, M.G., et al.: Bootstrap your own latent: A new approach to self-supervised learning. arXiv preprint arXiv:2006.07733 (2020) (5) Chen, X., He, K.: Exploring simple siamese representation learning (2020) (6) Zbontar, J., Jing, L., Misra, I., Lecun, Y., Deny, S.: Barlow Twins: Self-Supervised Learning via Redundancy Reduction. In: arXiv (2021). https://github.com/facebookresearch/barlowtwins (7) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020) (4) Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A., Guo, Z.D., Azar, M.G., et al.: Bootstrap your own latent: A new approach to self-supervised learning. arXiv preprint arXiv:2006.07733 (2020) (5) Chen, X., He, K.: Exploring simple siamese representation learning (2020) (6) Zbontar, J., Jing, L., Misra, I., Lecun, Y., Deny, S.: Barlow Twins: Self-Supervised Learning via Redundancy Reduction. In: arXiv (2021). https://github.com/facebookresearch/barlowtwins (7) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A., Guo, Z.D., Azar, M.G., et al.: Bootstrap your own latent: A new approach to self-supervised learning. arXiv preprint arXiv:2006.07733 (2020) (5) Chen, X., He, K.: Exploring simple siamese representation learning (2020) (6) Zbontar, J., Jing, L., Misra, I., Lecun, Y., Deny, S.: Barlow Twins: Self-Supervised Learning via Redundancy Reduction. In: arXiv (2021). https://github.com/facebookresearch/barlowtwins (7) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, X., He, K.: Exploring simple siamese representation learning (2020) (6) Zbontar, J., Jing, L., Misra, I., Lecun, Y., Deny, S.: Barlow Twins: Self-Supervised Learning via Redundancy Reduction. In: arXiv (2021). https://github.com/facebookresearch/barlowtwins (7) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. 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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. 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Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. 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Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Zbontar, J., Jing, L., Misra, I., Lecun, Y., Deny, S.: Barlow Twins: Self-Supervised Learning via Redundancy Reduction. 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International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. 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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. 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I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.04906 (8) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koch, G., Zemel, R., Salakhutdinov, R.: Siamese Neural Networks for One-Shot Image Recognition. In: ICML - Deep Learning Workshop, vol. 7, pp. 956–963 (2015) (9) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34(4), 802–808 (2018) (10) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. 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Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. 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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting (2020) (11) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. 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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive (2015) (12) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., Keogh, E.: The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018) (13) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. 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Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. 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Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020)
- Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for Self-Supervised Representation Learning (2020). https://github.com/htdt/ (14) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Hua, T., Wang, W., Xue, Z., Wang, Y., Ren, S., Zhao, H.: On Feature Decorrelation in Self-Supervised Learning. In: arXiv (2021). http://arxiv.org/abs/2105.00470 (15) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. 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In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020)
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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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(24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. 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In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020)
- Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020) (16) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huangi, L., Yang, D., Lang, B., Deng, J.: Decorrelated Batch Normalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 791–800 (2018). https://doi.org/10.1109/CVPR.2018.00089 (17) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. 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Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020)
- Franceschi, J.-Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019) (18) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 539–546 (2005). https://doi.org/10.1109/CVPR.2005.202. https://ieeexplore.ieee.org/abstract/document/1467314/ (19) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Van Den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2018) (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. 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In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/ (21) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised Feature Learning via Non-parametric Instance Discrimination. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 (22) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020)
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In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Dereniowski, D., Kubale, M.: Cholesky factorization of matrices in parallel and ranking of graphs. In: International Conference on Parallel Processing and Applied Mathematics, vol. 3019, pp. 985–992 (2003). https://doi.org/10.1007/978-3-540-24669-5{_} (23) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANS. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/AliaksandrSiarohin/wc-gan. (24) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017) (25) Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34(5), 1454–1495 (2020) (26) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020)
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Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. 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In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. 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Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. 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Advances in neural information processing systems 27 (2014) (27) Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: A unified approach to action segmentation. In: European Conference on Computer Vision, pp. 47–54 (2016). Springer (28) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. 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In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. 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In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) 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In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020)
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In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 448–456 (2015) (36) Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33, 9912–9924 (2020) Huang, L., Zhou, Y., Zhu, F., Liu, L., Shao, L.: Iterative normalization: Beyond standardization towards efficient whitening. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4869–4878 (2019). https://doi.org/10.1109/CVPR.2019.00501 (29) Wang, F.: Multi-Scale-1D-ResNet. GitHub (2018) (30) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019 (2019). https://github.com/loshchil/AdamW-and-SGDW (31) Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2017). https://github.com/loshchil/SGDR (32) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (33) Koohpayegani, S.A., Tejankar, A., Pirsiavash, H.: Mean Shift for Self-Supervised Learning. In: arXiv (2021). https://github.com/UMBCvision/MSFhttp://arxiv.org/abs/2105.07269 (34) Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022) (35) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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